Large data centers hosting possibly hundreds of services may span across many servers (physical or virtual). Customers or consumers of these hosted services may complain of slow or unacceptable performance. In order to track down the source causing the slowdown or degradation of performance, a team of information technology (IT) experts may be deployed. This team of IT experts may analyze application logs and server resource metrics to find the offending server in a given data center. Often, due to a possibly complex mapping of services to underlying server hardware, it may take up to several days to find the offending server causing the slowness. Complexity increases even more in virtualized cloud computing environments having services mapped to numerous virtual servers supported by physical servers in a data center. Traditional models for troubleshooting problematic servers may be based on mapping service level agreements (SLAs) to customer performance issues. Once the mapping is done, server resource metrics (e.g., processor, storage, memory, etc.) for servers in a data center are analyzed to identify probable candidate servers that may be the offending server. A next level of troubleshooting may include turning on additional levels of logging at application, middleware or infrastructure layers to identify a root-cause. However, due to the complex mapping of services to underlying virtual and/or physical infrastructure in a data center, no obvious correlation appears to exist between SLAs and the underlying virtual and/or physical infrastructure in the data center.